## ----include = FALSE---------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ## ----eval=F------------------------------------------------------------------- # install.packages("devtools") # devtools::install_github("luizesser/chooseGCM") ## ----eval=F------------------------------------------------------------------- # install.packages("chooseGCM") ## ----------------------------------------------------------------------------- library(chooseGCM) tictoc::tic() set.seed(1) ## ----eval=F------------------------------------------------------------------- # worldclim_data(path = "input_data/WorldClim_data_gcms_all", period = "future", variable = "bioc", year = "2090", gcm = "all", ssp = "585", resolution = 10) ## ----------------------------------------------------------------------------- var_names <- c("bio_1", "bio_12") s <- import_gcms(system.file("extdata", package = "chooseGCM"), var_names = var_names) ## ----------------------------------------------------------------------------- study_area <- terra::ext(c(-80, -30, -50, 10)) |> terra::vect(crs="epsg:4326") ## ----------------------------------------------------------------------------- res <- compare_gcms(s, var_names, study_area, k = 3) res$statistics_gcms ## ----------------------------------------------------------------------------- # Summary of GCMs s_sum <- summary_gcms(s, var_names, study_area) s_sum ## ----------------------------------------------------------------------------- # Pearson Correlation between GCMs s_cor <- cor_gcms(s, var_names, study_area, scale = TRUE, method = "pearson") s_cor ## ----------------------------------------------------------------------------- # Euclidean Distance between GCMs s_dist <- dist_gcms(s, var_names, study_area, method = "euclidean") s_dist ## ----------------------------------------------------------------------------- kmeans_gcms(s, var_names, study_area, k = 3, method = "euclidean") ## ----------------------------------------------------------------------------- kmeans_gcms(s, var_names, study_area, k = 3) ## ----------------------------------------------------------------------------- hclust_gcms(s, var_names, study_area, k = 3) ## ----------------------------------------------------------------------------- hclust_gcms(s, var_names, study_area, k = 3, n = 1000) ## ----------------------------------------------------------------------------- optk_gcms(s, var_names, study_area, cluster = "kmeans", method = "wss", n = 1000) ## ----------------------------------------------------------------------------- optk_gcms(s, var_names, study_area, cluster = "kmeans", method = "silhouette", n = 1000) ## ----------------------------------------------------------------------------- optk_gcms(s, var_names, study_area, cluster = "kmeans", method = "gap_stat", n = 1000) ## ----------------------------------------------------------------------------- montecarlo_gcms(s, var_names, study_area, perm = 10000, dist_method = "euclidean", clustering_method = "kmeans") ## ----------------------------------------------------------------------------- montecarlo_gcms(s, var_names, study_area, perm = 10000, dist_method = "euclidean", clustering_method = "closestdist") ## ----------------------------------------------------------------------------- env_gcms(s, var_names, study_area, highlight = res$suggested_gcms$k3) ## ----------------------------------------------------------------------------- env_gcms(s, var_names, study_area, highlight = "sum") ## ----------------------------------------------------------------------------- closestdist_gcms(s, var_names, study_area) ## ----------------------------------------------------------------------------- closestdist_gcms(s, var_names, study_area, k=3) ## ----------------------------------------------------------------------------- tictoc::toc()